Autonomous Robots Journal Special Issue:
Beyond Grasping - Modern Approaches for Dexterous Manipulation
The Autonomous Robots Journal invites papers for a special issue
entitled
"Beyond Grasping: Modern Approaches for Dexterous Manipulation". In
recent years,
grasping has matured to the point where various robots can reliably
perform
basic grasps on unknown objects in unstructured environments. While
this
achievement is a major milestone for robotics, it has not yet
translated into
major advances in manipulation. Instead, these robots are still far
from
human-level manipulation. They still lack many manipulation
skills, ranging from sequenced multi-object tasks (such as stacking and
tool usage)
to bimanualor in-hand manipulations of objects and interactions with
non-rigid objects.
As these manipulations involve interacting with uncertain real-world
environments,
they pose major problems for many current approaches and traditional
methods that
depend on accurate models of the robot and its surrounding. Hence,
there is strong
need for more advanced methods that can manipulate objects in the face
of uncertainty.
Autonomous Robots seeks submissions Special Issue on “Beyond Grasping –
Modern Approaches for Dextrous Manipulation”. This special issue
focuses on how modern
sensors data processing algorithms, movement generation approaches or
learning methods
can help robots go beyond basic grasping abilities towards more
advanced dextrous
manipulation skills. We invite submissions of research papers that
address
important challenges in robot manipulation. We also solicit submissions
that rigorously
discuss and compare current state of the art techniques, as well as
recent advances in
the field, or open challenges.
Important Dates:
* Paper submission deadline: January 15th, 2013
* First reviews completed: April 1st, 2013
* Revised papers due: April 30th, 2013
* Final decision: June 1st, 2013
Topics of interest include but are not limited to:
- What is the state-of-the-art in robot learning of manipulations?
- How can we benefit from recent results in machine learning, e.g.,
structured learning, Gaussian processes, conditional random fields,
deep
belief networks?
- How can robots make use of reinforcement learning, or other
self-improvement methods, to adapt to changing environments and tasks?
- How can robots learn to handle ambiguous sensory signals?
- How can robots model uncertainty in their surroundings and their
actions?
- Which representations can leverage the acquisition of complete
multi-modal models of the environment?
- How can robots perform bimanual actions that are synchronized?
- How can robots determine optimal actions on non-rigid objects?
- How can robots learn to robustly detect the salient events in
manipulation tasks, e.g. when objects make and break contact?
- What is the state of the art in robot hand technology?
- How much can we reliably learn from simulations?
- How can apprenticeship learning help to overcome the correspondence
problem?
- How can robots remove and place complex objects in cluttered
environments?
- How can we model finger synergies over longer action sequences?
- How can human task knowledge be efficiently transferred to robots?
- How can task-relevant features of objects be estimated?
- How can robots efficiently generalize a task from only a few human
demonstrations?
- How can a robot represent compound objects; e.g. objects stacked on
a tray or a bottle and a cap?
- How can the effects of actions be represented in a general form?
- What prior knowledge can a robot be expected to have?
- What are the key challenges and can we decide on benchmark tasks that
allow us to measure and compare progress in this field?
- Which datasets and code components can be shared, in order to allow
researchers to compare their respective methods and build upon each
other's work?
Guest Editors:
Heni Ben Amor (amor at ias.tu-darmstadt.de) - TU Darmstadt
Nicolas Hudson (Nicolas.H.Hudson at jpl.nasa.gov) - NASA Jet Propulsion
Laboratory
Ashutosh Saxena (asaxena at cs.cornell.edu) - Cornell University
Jan Peters (peters at ias.tu-darmstadt.de) - MPI for Intelligent
Systems/TU Darmstadt
Submission:
Papers must be prepared in accordance with the AURO guidelines.
http://www.springer.com/engineering/robotics/journal/10514
All papers will be reviewed following the regular reviewing procedure
of
the Journal.
For more information, contact: amor at ias.tu-darmstadt.de